Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency |
Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency | Fair AI Network | Descentralization | Transparency |
Artificial intelligence requires vast resources:
Data: Large datasets are essential for training and fine-tuning AI models.
Computation: Large computing resources are needed to process complex algorithms and data.
Human Annotation: Expert human input guides the learning process, and ensures the accuracy.
Fairness in AI is critical. We record predictions on the network to evaluate fairness constantly. This allows users to have transparent model evaluation metrics.
The network facilitates the fine-tuning of models on-chain. New versions of the model and data are available and constantly evaluated for fairness.
Creates a traceable history of how models evolve over time and how different datasets influence their development.
Earn | Pay |
---|---|
Usage of your models | Deploying a model |
Usage of your data | Fine-tuning |
Helping validate fairness in AI models | Making predictions on the network |